Extant data integration processes generally require that data sources are specified explicitly for a particular task. In other words, integration rules defined over multiple sources may be pre-defined and static. Updating these rules may take a significant amount of re-development or re-processing time. An additional focus of extant techniques for data integration is on identifying and ensuring the unique identity of data across data sources. As a consequence, a holistic view of the data is difficult to get, as each data source is treated as an individual entity rather than as an entity related to other entities in the set.
In defining static rules for dealing with data, existing technologies may lack context based understanding, and, as a consequence, fail to recognize semantically related data during integration. Lacking an ability to resolve the semantic relevance of associated data, existing data integration and value upgrade tasks may not be truly complete, as they may fail to bring together related data sources. An additional consequence may include an inability to organize data in such a way that retrieved information is confined to a particular theme that is related to an input query.
In the absence of semantic relationships between data, tracing the movement of data across a data value chain may constitute an enormous effort without a record of which version of data evolved from which other version.
Accordingly, there is a need for a system and method for data integration that is able to bring semantically related data together for a given context.